What data for this category looks like
Data for this category primarily comes from crowdsourcing platform backends, annotation quality spot check records, annotator training exam results, and financial settlement systems. Daily maintenance documents include personnel access rules, training assessment materials, monthly performance reports, and settlement detail lists. Data update frequencies are divided into daily and monthly updates. Daily update data includes same-day annotation quality and personnel attendance records. Monthly update data includes performance rankings and quarterly settlement details. Data fields include unique personnel identifiers, qualification review status, exam scores, task completion volume, settlement amount, and other related items.
How these characteristics create constraints during deployment and upgrade
Data sources for this category are scattered, involving multiple sets of data sources such as crowdsourcing platforms, quality spot check systems, and financial systems. During deployment, configure multi-source data connection parameters to ensure consistent data synchronization logic across different systems. Data update frequencies include both daily and monthly cycles. During upgrade, adjust the trigger interval of scheduled tasks to avoid excessive server resource usage from high-frequency tasks. Daily documents include sensitive information such as personnel qualifications and performance details. During deployment, configure permission controls and storage paths to prevent data leaks. Unlike text annotation projects, this category focuses more on workforce organization and performance settlement. Therefore, configure exclusive permissions for settlement data during deployment. Data errors directly lead to quality fluctuations or settlement mistakes. During the deployment phase, preset data validation rules to reduce abnormal risks.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
DB_MAX_LINK | 100–150 connections | Meets concurrent connection requirements for FastGPT v4.17.0, supports multi-source data synchronization |
MONGODB_URI | Exclusive database address pointing to annotation quality records | Stores daily update data such as crowdsourcing platform annotation quality and personnel attendance records |
PG_URL | Database address pointing to performance and settlement data | Stores structured data such as monthly performance reports and settlement lists |
REDIS_URL | Cache rule configured with 600-second expiration | Caches frequently accessed personnel qualification and access rule documents to reduce database pressure |
STORAGE_S3_ENDPOINT | Enterprise-owned object storage node, reserve 1000 MB or more storage space | Stores document-type data such as training materials and exam results |
FE_DOMAIN | Public network domain name for deployed services | Configures frontend access address, ensures correct domain validation for multi-account team logins |
Three common mistakes
- Symptom: Frontend page returns 502 status code, cannot access OneApi-related functional modules. Cause: Failed to correctly configure domain mapping between
FE_DOMAINand backend services, resulting in blocked cross-origin requests. - Symptom: After logging into a multi-account team, cannot view performance reports or settlement list data, page fields are empty. Cause: Failed to configure team data access permissions for the database corresponding to
PG_URL, resulting in inability to read structured performance and settlement data. - Symptom: Daily annotation quality data synchronization task times out, task status shows failure. Cause:
DB_MAX_LINKis set too low, database connection pool cannot support concurrent requests for multi-source data synchronization.
How to confirm configurations are correct
- Run a database connection pool test, observe connection usage corresponding to
DB_MAX_LINK, and adjust to a range that meets business concurrency requirements. - Access the frontend address configured in
FE_DOMAIN, test the multi-account team login function, and verify normal access to performance and settlement data. - Trigger a daily data synchronization task, check that synchronization logs have no errors, and confirm normal multi-source data connection.
- Upload a training material document, verify that the storage node corresponding to
STORAGE_S3_ENDPOINTcan normally store and read files.
The values provided here are common starting points, and should be evaluated against the reader’s own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-10-19.